Optimally Trained Regression Trees and Occam's Razor

0404694 - UIVT-O 20020058 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Savický, Petr - Klaschka, Jan
Optimally Trained Regression Trees and Occam's Razor.
COMPSTAT 2002. Proceedings in Computational Statistics. Heidelberg: PhysicaVerlag, 2002 - (Härdle, W.; Rönz, B.), s. 479-484. ISBN 3-7908-1517-9.
[COMPSTAT 2002. Berlin (DE), 24.08.2002-28.08.2002]
Grant CEP: GA ČR GA201/00/1482
Výzkumný záměr: AV0Z1030915
Klíčová slova: regression trees * recursive partitioning * optimization * dynamic programming * bottom-up algorithms * generalization * Occam's razor
Kód oboru RIV: BA - Obecná matematika

Two bottom-up algorithms growing regression trees with the minimum mean squared error on the training data given the number of leaves are described. As demonstraded by the results of experiments with simulated data, the trees resulting from the optimization algorithms may have not only better, but also worse generalization properties than the trees grown by traiditional methods. This phenomenon is discussed from the point of view of the Occam's razor principle.
Trvalý link: http://hdl.handle.net/11104/0124933